![Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5943/TOC-Subject-Sample-2021-Mathematics-Statistics.jpg"})
ABSTRACT
In this paper, two attribute control charts under multiple dependent state sampling (MDS) and its modification are proposed to monitor the multivariate Poisson count data. The sensitivity of the MDS is increased by introducing an additional parameter. The new method is named as generalized multiple dependent state sampling (GMDS). The proposed charts are based on two pairs of limits i.e. inner and outer control limits that make use of the past samples information in addition to the current sample. The monitoring statistics of the proposed charts are the sum of non-conformities or flaws of each quality characteristics that make the charting structure relatively simple. The performance comparison of the proposed charts with the existing multivariate Poisson control chart is made by using out-of-control average run length (ARL). The comparison reveals that the ARLs of the proposed charts are considerably smaller than the existing scheme for all shift sizes and different values of parameters. Simulation study also showed the dominance of the proposed charts over other existing control charts in detecting the process shift. For practical application, an empirical illustration is provided to signify the performance evaluation of the proposed charts.
Acknowledgments
The authors are deeply thankful to reviewers and editor for their valuable suggestions to improve the quality of paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Muhammad Ali Raza
Muhammad Ali Raza obtained his MSc and MPhil degrees in Statistics from the University of the Punjab Lahore, Pakistan. He is working as an Assistant Professor in the Department of Statistics, Government College University, Faisalabad, Pakistan since October 2011. Currently, he is enrolled in PhD (Statistics) from the School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, P. R. China under the Chinese Government Scholarship Program (2015). His research interest includes Statistical Process Control and Applied Statistics.
Muhammad Aslam
Muhammad Aslam has received meritorious services award in research from National College of Business Administration & Economics Lahore in 2011. He received Research Productivity Award for the year 2012 by Pakistan Council for Science and Technology. His name Listed at 2nd Position among Statistician in the Directory of Productivity Scientists of Pakistan 2013. His name Listed at 1st Position among Statistician in the Directory of Productivity Scientists of Pakistan 2014. He got 371 positions in the list of top 2210 profiles of Scientist of Saudi Institutions 2016. He is selected for “Innovative Academic Research & Dedicated Faculty Award 2017” by SPE, Malaysia. He Received King Abdulaziz University Excellence Awards in Scientific Research for the paper entitled “Aslam, M., Azam, M., Khan, N. and Jun, C.-H. (2015). A New Mixed Control Chart to Monitor the Process, International Journal of Production Research, 53 (15), 4684–4693. He Received King Abdulaziz University citation award for the paper entitled “Azam, M., Aslam, M. and Jun, C.-H. (2015). Designing of a hybrid exponentially weighted moving average control chart using repetitive sampling, International Journal of Advanced Manufacturing Technology, 77:1927–1933 in 2018. He is the member of editorial board of Electronic Journal of Applied Statistical Analysis, Asian Journal of Applied Science and Technology and Pakistan Journal of Commence and Social sciences. He is also member of Islamic Countries Society of Statistical Sciences. He is appointed as an external examiner for 2016/2017-2018/2019 triennium at The University of Dodoma, Tanzania. His areas of interest include reliability, decision trees, Industrial Statistics, acceptance sampling, rank set sampling and applied Statistics.